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anjc
If you're the author, I'm not sure that the definition of recall@k is correct.

>Recall@k is a metric evaluate how many items, in the recommendation list, are relevant (hit) in the ground-truth data.

hoaphumanoid
thanks for the comment, would this definition be clearer: "Recall at k is the proportion of relevant items found in the top-k recommendations"?
emourkai
this really should've been named "recommendations for building recommendation systems"
hoaphumanoid
hey one of the authors here. We are planning to create some notebooks on recommendation about recommendation algos :-)
yueguoguo1024
LOL brilliant idea! just don’t hesitate to create an issue and follow that with a PR!
tandav
Sadly but evaluation metrics are only implemented in pyspark.mllib (RDD API) but not in pyspark.ml (Dataframe API)

Also worth mention about implicit feedback

yueguoguo1024
It is also my sadness - AFAIK pyspark.ml does not support ranking metrics (does it?). So we wrapped the RDD and expose data frame as interfaces in our own implementations (the `reco_utils`). Implicit feedback is mentioned in a notebook under `staging` branch (https://github.com/Microsoft/Recommenders/blob/staging/noteb...). Will be there soon in the next release (1st of Feb)!